Image Classification Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Image Classification projects give practical experience and help complete final-year submissions. All projects follow IEEE standards and each project includes source code, project thesis report, presentation, project execution and explanation.

Image Classification Final Year Projects

An Attention-Based Convolutional Neural Network for Intrusion Detection Model

This project focuses on improving network security by detecting intrusions quickly and accurately. It uses a type of artificial intelligence called convolutional neural networks with attention mechanisms. The method organizes network data into images in a smart way to make the detection process faster. Experiments show that this approach can identify threats efficiently while keeping high accuracy.
An Automated Chest X-Ray Image Analysis for Covid-19 and Pneumonia Diagnosis using Deep Ensemble Strategy

This project develops an AI-based system to detect Covid-19 and pneumonia from chest X-ray images. It uses advanced deep learning models to analyze images and identify diseases more accurately than traditional methods. The system improves image data with techniques like rotation and flipping and combines multiple models to make reliable predictions. Experiments show it achieves around 97% accuracy, helping doctors make faster and better treatment decisions.
An Improved Densenet Deep Neural Network Model for Tuberculosis Detection Using Chest X-Ray Images

This project focuses on detecting tuberculosis (TB) from chest X-ray images using a new deep learning model called CBAMWDnet. The model combines advanced techniques to better understand important features in the images. Tests on large datasets show it is very accurate and performs better than many existing models. This approach can help doctors diagnose TB earlier and more reliably.
Bit-Plane and Correlation Spatial Attention Modules for Plant Disease Classification

This project focuses on automatically identifying plant diseases using artificial intelligence. It improves existing deep learning methods by adding a special attention model that focuses on the most important parts of plant images. The model detects disease areas more accurately and achieves very high accuracy on public plant disease datasets. The experiments show it works better than many commonly used methods.
Classification of Liver Fibrosis From Heterogeneous Ultrasound Image

This project studies how artificial intelligence can help doctors diagnose liver problems using ultrasound images. The researchers found that AI models work well on images similar to those they were trained on but perform worse on images from different machines. They also explored ways to reduce errors caused by differences between machines and improved classification by combining similar categories. The work highlights the need for AI that performs reliably across different ultrasound devices.
Bone Stick Image Classification Study Based on C3CA Attention Mechanism Enhanced Deep Cascade Network

This project focuses on classifying ancient bone sticks unearthed in China using artificial intelligence. It develops a deep learning model that can accurately identify fracture locations and colors on the bone sticks. The model uses advanced attention techniques to focus on important features and reduce background interference. As a result, it achieves high accuracy, making the classification of these historical artifacts much faster and more reliable.
Data Augmentation Based on Generative Adversarial Networks for Endoscopic Image Classification

This project aims to help doctors detect digestive system diseases more easily using computer-based image analysis. The system trains several deep learning models to automatically classify diseases from endoscopy images. It also creates extra training images using generative models to improve accuracy. The final model shows strong and safe performance, reducing the workload on medical staff.
Gated Deep Reinforcement Learning With Red Deer Optimization for Medical Image Classification

This project focuses on improving brain tumor detection from MRI images using artificial intelligence. The researchers developed a deep learning model that automatically processes and classifies images, reducing errors and saving time. They used special filters and mathematical techniques to extract important features from the images. The model achieved very high accuracy of 98.8% when tested on brain tumor data.
Malaria Disease Cell Classification With Highlighting Small Infected Regions

This project uses deep learning to detect malaria from images of red blood cells. The researchers created a method that focuses on the small infected regions in the cells, similar to how humans highlight important information. Their approach improved the accuracy of malaria detection on a public dataset to 97.2%, which is higher than standard models. The study shows that focusing on key areas in the images helps the neural network learn better.
Multi-Class Retinal Diseases Detection Using Deep CNN With Minimal Memory Consumption

This project focuses on using machine learning to identify and classify eye diseases. The researchers designed a new neural network model that works efficiently without using too much memory. They tested it on a dataset containing 32 types of retinal diseases. The model performed very well, achieving 95% accuracy while managing resources better than previous methods.
Skin Medical Image Captioning Using Multi-Label Classification and Siamese Network

This project develops a system that can automatically describe skin images using simple sentences. It uses multiple machine learning models to identify skin features, match keywords, and relate them to everyday language descriptions. The system achieved very high accuracy and can help teach dermatology, especially in hospitals or schools with limited resources. It makes learning skin diagnosis easier and supports practical training for medical students.
A Review on Role of Image Processing Techniques to Enhancing Security of IoT Applications

This project reviews how image processing can make Internet of Things (IoT) applications safer and more private. It studies recent research that uses images to protect sensitive data in IoT systems. The work also presents a clear framework showing which image techniques are best for enhancing IoT security. It helps researchers understand and choose the right methods for secure IoT applications.
A Deep Learning-Based Efficient Firearms Monitoring Technique for Building Secure Smart Cities

This project focuses on automatically detecting guns and human faces in videos and images using deep learning. It combines multiple detection methods to improve accuracy. The system can help police quickly identify violent incidents and monitor social media for gun-related content. It works reliably and performs better than single detection models.
A hybrid method for identifying the feeding behavior of tilapia

This project focuses on monitoring how tilapia fish eat in real time. The researchers improved a computer vision model called ResNet34 to better recognize fish feeding behavior. They added a module to help the model focus on important image features and used transfer learning to speed up training. The final model achieved very high accuracy, helping farmers decide the right amount of feed scientifically.
Enhancing Few-Shot Image Classification With Cosine Transformer

This project focuses on teaching a computer to recognize images even when it has very few examples to learn from. The researchers developed a new method called Few-shot Cosine Transformer, which compares a small set of labeled images with new unlabeled images to improve accuracy. They use a special attention mechanism called Cosine Attention to make the model more reliable and efficient. This approach works well on standard datasets and can be applied in areas like healthcare, security, and pose recognition.
Experimental Validation of Artificial Neural Network Based Road Condition Classifier and its Complementation

This project focuses on using artificial intelligence to estimate how slippery a road is for each wheel of a car. The researchers improved an existing neural network by adding braking pressure as an input. They tested the system using real-world data and found it predicts road friction accurately in normal and challenging conditions. This method uses only sensors already in most cars, so no extra equipment is needed.
Recurrent Residual Networks Contain Stronger Lottery Tickets

This project shows that large neural networks can be simplified without training by selecting smaller subnetworks from them. These small networks are sparse, use fewer values, and can run efficiently on hardware. The study finds that converting some networks into recurrent forms improves accuracy and reduces memory use. Using this method, a popular network can be shrunk almost 50 times while keeping good performance.
Water Classification Using Convolutional Neural Network

This project focuses on classifying different water sources using images. The researchers improved the images’ quality using special techniques to make textures and contrasts clearer. They then used a new neural network called WaterNet to identify the water types. Their method achieved 97% accuracy and performed better than existing popular models.

Interested in any of these final year projects?

To get more project ideas, documentation, source code & expert guidance..

Enquire Now

How We Help You with Image Classification Projects

At Final Year Projects, we provide complete guidance for Image Classification IEEE projects for BE, BTech, ME, MSc, MCA and MTech students. We assist at every step from topic selection to coding, report writing, and result analysis.

Our team has over 10 years of experience guiding students in Computer Science, Electronics, Electrical, and other engineering domains. We support students across India, including Hyderabad, Mumbai, Bangalore, Chennai, Pune, Delhi, Ahmedabad, Kolkata, Jaipur and Surat. International students in the USA, Canada, UK, Singapore, Australia, Malaysia, and Thailand also benefit from our expert guidance.

Image Classification Project Synopsis & Presentation

Final Year Projects helps prepare Image Classification project synopsis, including problem statement, objectives, existing system, disadvantages, proposed system, advantages and research motivation. We provide PPT slides, tutorials, and full documentation for presentations.

Image Classification Project Thesis Writing

Final Year Projects provides thesis writing services for Image Classification projects. We help BE, BTech, ME, MSc, MCA and MTech students complete their final year project work efficiently.

All theses are checked with plagiarism check tools to guarantee originality and quality. Fast-track services are available for urgent submissions. Hundreds of students have successfully completed their projects and theses with our support.

Image Classification Research Paper Support

We offer complete support for Image Classification research papers. Services include writing, editing, and proofreading for journals and conferences.

We accept Word, RTF, and LaTeX formats. Every paper is reviewed to meet IEEE and publication standards, improving acceptance chances. Our guidance ensures that students produce high-quality, publication-ready research papers.

Reach out to Final Year Projects for expert guidance on Image Classification projects. Get support for coding, reports, theses, and research publications. Contact us via email, phone, or website form and start your project with confidence.

Image Classification Final Year Projects with Source Code